I built a Math Solver App using Google's Gemini Model with Flutter

This paragraph introduces the concept of creating an app using Google's Gemini model to solve math equations with step-by-step explanations. The speaker acknowledges the delay in creating this video due to the desire to build a practical application. The app development process involves using Flutter for its ease of use, capturing an image of an equation with a camera, sending the image to Gemini via API, and displaying the response in a text widget. The video will not cover Flutter setup as there are already good resources available, but will focus on the unique aspects of this project, such as setting up the UI, adding dependencies for image handling, and using stateful widgets for dynamic content updates.

The second paragraph delves into the process of integrating camera functionality and image cropping within the app. It details the use of the image picker dependency to capture images and the image cropper for refining the captured image. The speaker guides through the implementation of a floating action button to trigger the camera, handling the image capture, and updating the UI to reflect the captured image. The paragraph also covers the challenges of handling multiple equations in a single image and the solution to crop the image to focus on a single equation. Additionally, it touches on the necessary configurations for the Android manifest file to enable image cropping and the process of updating the UI to accommodate the cropped image.

The third paragraph focuses on setting up the Gemini API to process the cropped images of equations. It explains the process of obtaining an API key from Google AI Studio and configuring the API request payload. The speaker describes the steps to encode the image in base64 format, prepare the JSON payload, and send a POST request to the Gemini API. The paragraph also highlights the importance of using the correct URL for Gemini Pro Vision, which is designed to handle image inputs. It discusses the debugging process to ensure the API request is successful and the app captures, crops, and sends images for equation solving.

The final paragraph discusses the final stages of the app development, including displaying the results from the Gemini API and enhancing user interaction. It covers the creation of a text widget to display the solution and the implementation of a loading bar to indicate the processing of the API request. The speaker also introduces the concept of a custom prompt, allowing users to specify the type of mathematical problem they want to solve. The paragraph concludes with a demonstration of the app's functionality, showing how users can capture an image of an equation, crop it, set a custom prompt, and receive a step-by-step solution from the Gemini API. The video ends with an invitation for viewers to ask questions and suggest topics for future videos.